DE102025116826A1 - Estimating the concentration of a respiratory gas in a patient's blood - Google Patents

Abstract

A method for estimating the concentration of a respiratory gas in a patient's blood comprises: receiving measurement data showing a volume-dependent profile of the concentration of the respiratory gas in a patient's exhaled airflow as a function of the patient's exhaled air volume; generating input data (14) from the measurement data, wherein the input data (14) comprise a matrix of values for various parameters relating to the volume-dependent profile; inputting the input data (14) into a machine learning module (15) trained to convert the input data (14) into output data (17), wherein the output data (17) indicate a concentration of the respiratory gas in the patient's blood; and outputting the output data (17) by the machine learning module (15).

Description

Technical field

The invention relates to a method for estimating the concentration of a respiratory gas in a patient's blood. Furthermore, the invention relates to a method for training a machine learning module for use in such a method. The invention also relates to a data processing device, a computer program, and a computer-readable medium for executing at least one of these methods, as well as a medical device.

State of the art

During mechanical ventilation of anesthetized and critically ill patients, the proper exchange of biological gases must be continuously maintained. The gold standard for assessing gas exchange in the clinical setting is the analysis of biological gases in arterial blood samples. This involves comparing the partial pressures of carbon dioxide and oxygen in the blood with the proportion of inhaled oxygen and alveolar ventilation to determine whether or not respiratory failure is present. However, such blood gas analysis is invasive and time-consuming.

In addition, it is possible to measure the alveolar partial pressure of carbon dioxide (p_{_A} CO _₂) non-invasively using capnography. However, such a measurement cannot yet replace conventional blood gas analysis, especially in anesthetized and critically ill patients.

Disclosure of the invention

One object of the invention can be seen as providing a method that makes it possible to determine the concentration of a respiratory gas in a patient's blood non-invasively and with sufficient accuracy. A further object of the invention can be seen as providing a method for training a corresponding machine learning module, a corresponding data processing device, a corresponding computer program, a corresponding computer-readable medium, and a corresponding medical device.

These problems are solved by the subject matter of the independent claims. Advantageous embodiments of the invention are set out in the dependent claims, the following description, and the accompanying figures.

A first aspect of the invention relates to a computer-implemented method for estimating the concentration of a respiratory gas in a patient's blood. The method comprises: receiving measurement data showing a volume-dependent profile of the concentration of the respiratory gas in an exhaled airflow from the patient, depending on the volume of air exhaled by the patient; generating input data from the measurement data, wherein the input data comprise a matrix of values for various parameters relating to the volume-dependent profile; feeding the input data into a machine learning module trained to convert the input data into output data, wherein the output data indicate a concentration of the respiratory gas in the patient's blood; and outputting the output data by the machine learning module.

A second aspect of the invention relates to a computer-implemented method for training a machine learning module for a medical device. The method comprises: receiving multiple measurement data sets, each containing measurement data showing a volume-dependent profile of the concentration of a respiratory gas in an exhaled airflow from a patient, depending on the volume of air exhaled by the patient, wherein the measurement data from different data sets are at least partially assigned to different patients; generating multiple training data sets from the measurement data sets, each training data set being assigned to one of the patients and comprising a matrix of values for various parameters relating to the volume-dependent profile; inputting each training data set as input data into the machine learning module, which is configured to convert the input data into output data, wherein the output data shows a concentration of the respiratory gas in the blood of the respective patient; outputting the output data by the machine learning module; and determining a deviation of the output data from target data assigned to the respective (underlying) training data set. Adjusting the weights of the machine learning module in an optimization process to reduce the deviation.

It is possible that the methods are executed automatically by a processor, for example, by a processor of a medical device. The machine learning module used in the method according to the first aspect of the invention may have been trained using the method according to the second aspect of the invention. The method according to the first aspect of the invention may additionally include the steps the method according to the second aspect of the invention.

The approach presented here is based on the understanding that the concentration of a respiratory gas in a patient's blood, particularly in their arterial blood, can be estimated by analyzing the curve of a volumetric capnogram or oxigram using a machine learning algorithm. This curve changes depending on fluctuations in the patient's lung ventilation and perfusion. Such fluctuations can lead to significant inaccuracies when estimating with conventional methods.

In contrast, the methods described above and below allow for a very accurate estimation of the concentration of the respiratory gas in the patient's blood, even in different patients and/or with significant fluctuations in lung function. This has the advantage that invasive blood gas analysis needs to be performed less frequently or may even be unnecessary.

Compared to an embodiment in which the concentration of the respiratory gas in the patient's blood is estimated directly from the (raw) measurement data, or in which the (raw) measurement data is used as input data, the methods described above and below have the additional advantage of requiring significantly less computing power and enabling a sufficiently accurate estimate even with less-than-ideal measurement data. Furthermore, the risk of misinterpretations is reduced because the input data, unlike (raw) measurement data, is predefined.

It is particularly advantageous to analyze multiple parameters of the curve. Such a multivariate approach provides more information for estimation than analyzing only a single parameter, such as the alveolar partial pressure of the relevant respiratory gas, and, in combination with a suitably configured machine learning algorithm, enables a significantly more accurate and robust estimation.

Some terms are explained in more detail below.

The term "patient" can be understood to mean a ventilated patient, i.e., a human or animal subject who is or is to be ventilated by means of a ventilator.

The term "breathing gas" can refer to, for example, one of the following gases: carbon dioxide, oxygen, nitrogen, water vapor, anesthetic gas.

The term "breathing air," as in "breathing airflow" or "breathing air volume," can refer to a mixture of breathing gases.

The term "concentration" can generally be understood as a proportion or quantity, in particular a partial pressure.

The concentration of the respiratory gas in the patient's blood can, for example, be an arterial and/or venous partial pressure of the respiratory gas.

The measurement data may have been generated, at least in part, using appropriate sensors, such as those of a medical device. For example, the measurement data may have been generated non-invasively through capnography and/or oxigraphy. In other words, the measurement data may consist of data from a capnogram and/or an oxigram.

The measurement data underlying the training datasets can include real data (i.e., data resulting from a real measurement) and/or simulated data. The simulated data—unlike the real data—may have been generated using a simulation environment in which the lung conditions of various patients are simulated by a computer.

The term "input data" can refer to data that differs from the measurement data and/or, unlike the measurement data, is specifically adapted to the machine learning module. In particular, the input data can be compressed data compared to the measurement data. It is possible that the measurement data is fed into another machine learning module that has been trained to convert the measurement data into the input data.

A "machine learning module" can be understood as a hardware and/or software module for converting input data into output data in a machine learning-parameterized, i.e., trained, algorithm. Such an algorithm could be, for example, an artificial neural network, a decision tree, a random forest, a k-nearest neighbors algorithm, a support vector machine, a Bayesian classifier, a k-means algorithm, a genetic algorithm, a kernel regression algorithm, a discriminant analysis algorithm, or a combination of at least two of these examples.

Each training dataset entered into the machine learning module can be associated with a set of predefined target data. Target data may, for example, include a target value for the concentration of respiratory gas in the blood of a given patient, associated with the respective training dataset. Specifically, the target data may show the result of a measurement (e.g., a blood gas analysis) performed at the same time and/or shortly before and/or after the time at which the measurement data underlying the respective training dataset were generated. The target data may include real data (i.e., resulting from a real measurement) and/or simulated data. The simulated data—unlike the real data—may have been generated using a simulation environment in which a computer simulates the lung conditions of various patients, along with the respective training dataset.

To determine the deviation of the output data from the target data, the output data and the target data can be input into a suitable loss function to calculate a score quantifying the deviation. The score can be calculated, for example, using the method of least squares.

The term "optimization method" can be understood as an iterative procedure for minimizing the loss function, for example a gradient method, in particular a stochastic gradient method, with backpropagation.

A third aspect of the invention relates to a data processing device. The data processing device comprises a processor configured to perform at least one of the methods described above and below.

The term "data processing device" can generally be understood to mean a computer. A data processing device can comprise hardware and/or software components. For example, a data processing device can be a control unit, a PC, a server, a laptop, a tablet, a smartphone, or a combination of at least two of these examples. Alternatively, "data processing device" can be understood to mean at least one hardware and/or software component of at least one of these examples.

The term "processor" can refer to, for example, a CPU (central processing unit), a graphics processor, a TPU (tensor processing unit), or a combination of at least two of these examples.

In addition to the processor, the data processing device may include at least one of the following components: a memory, a bus system for data communication between the processor and the memory, a data communication interface for wireless and/or wired data communication with peripheral devices.

It should be noted that features of the procedures described above and below may also be features of the data processing device (and vice versa).

A fourth aspect of the invention relates to a medical device. The medical device comprises sensors for generating measurement data that indicate a volume-dependent profile of the concentration of a respiratory gas in an exhaled airflow from a patient, depending on the volume of air exhaled by the patient, as well as a data processing device as described above and below.

The medical device could be, for example, a ventilator for invasive and/or non-invasive ventilation of a patient and/or a monitoring device for monitoring a patient's vital parameters.

The sensor system can include one or more gas sensors. A "gas sensor" can be, for example, a galvanic, paramagnetic, or optical sensor. Such a gas sensor can be located in a main and/or sidestream of the exhaled air and/or be designed as a pressure and/or flow sensor.

Further aspects of the invention relate to a computer program and a computer-readable medium on which the computer program is stored.

The computer program includes instructions that, when the computer program is executed by the processor, cause a processor (for example, the processor of the data processing device described above and below) to perform at least one of the procedures described above and below.

The computer-readable medium can be volatile or non-volatile data storage. For example, the computer-readable medium can be a hard drive, a USB (universal serial bus) storage device, RAM (random-access memory), ROM (read-only memory), EPROM (erasable programmable read-only memory), EEPROM (electrically erasable programmable read-only memory), flash memory, or a combination thereof. It must be one of at least two of these examples. The computer-readable medium can also be a data communication network that allows the downloading of program code (e.g., via the internet), or a cloud.

It should be noted that features of the procedures described above and below may also be features of the computer program and/or the computer-readable medium (and vice versa).

The following describes various embodiments of the invention. These embodiments are not to be understood as limiting the scope of the invention.

According to one embodiment, the measurement data can display the volume-dependent curve related to a single breath of the patient. In other words, each point of the volume-dependent curve can be assigned a specific fraction of the total volume of air containing the respiratory gas exhaled by the patient during a single breath. Accordingly, the beginning of the volume-dependent curve can be assigned a volume of zero, and the end of the volume-dependent curve a volume equal to the total volume. Such a total volume can also be referred to as the tidal volume. It is possible that the measurement data were generated during the single breath and/or are newly generated and/or received with each breath.

In other words, the measurement data can include a series of concentration values for the concentration of the respiratory gas in the airflow and a series of volume values for the respiratory air volume. Each volume value can be a value from a range bounded by a lower and an upper limit, where the lower limit is zero and the upper limit indicates the total volume exhaled by the patient in a single breath, with each concentration value corresponding to a different volume value.

According to one embodiment, the measurement data can further indicate a positive end-expiratory pressure (PEP) associated with the volume-dependent curve for ventilating the patient. In this case, the input data can include the PEP and/or be generated taking the PEP into account. This allows for a more accurate estimation compared to an embodiment that does not consider the PEP.

According to one embodiment, a mathematical function that approximately defines at least one section of the volume-dependent curve can be determined using the measurement data. At least one of the values in the matrix can be calculated using this mathematical function. Suitable parameters can be determined in a predictable and transparent manner using this mathematical function. The mathematical function can be a single mathematical function or a combination of several individual mathematical functions.

According to one embodiment, the input data can comprise a matrix of values for 2 to 20, 10 to 20, or 10 to 15 different parameters relating to the volume-dependent curve. In this way, the consumption of computing resources can be significantly reduced compared to an embodiment with larger input matrices.

The term "parameter" can refer to any ventilation parameter relevant to a patient's ventilation, both before and after the initial entry. Each value in the matrix can be assigned to one of the various parameters. Accordingly, depending on the number of different parameters, the matrix might contain, for example, 2 to 20, 10 to 20, or 10 to 15 input values. The matrix can be interpreted as a one-, two-, or three-dimensional vector. For example, the machine learning module could be configured to convert these input values into a single output value indicating the concentration of the respiratory gas in the patient's blood.

According to one embodiment, the measurement data may have been generated in several successive time steps. In this case, the mathematical function can be determined using the measurement data from different time steps, for example, by regression. A single time step could, for example, last as long as a single breath of the patient. However, the respective duration of the time steps can also be fixed and/or be, for example, on the order of 1 ms, 10 ms, 100 ms, 1 s, or 10 s.

According to one embodiment, the mathematical function can be determined using the Levenberg-Marquardt algorithm. The Levenberg-Marquardt algorithm can be understood as a special numerical optimization algorithm for solving nonlinear least-squares problems using the method of least squares. The algorithm can be described as a combination of the Gauss-Newton method with a regularization technique. This technique, which forces descending function values, can be understood as a more accurate and computationally efficient approximation than an implementation of the classical Fowler method, even with larger fluctuations in the volume-dependent curve between successive breaths and/or between different patients.

According to one embodiment, the various parameters may include at least one of the following: a total volume of respiratory gas exhaled by the patient in a single breath; a total volume of respiratory air containing the respiratory gas exhaled by the patient in a single breath (also called tidal volume); a minute ventilation; alveolar ventilation; an airway dead space; a mixed expiratory partial pressure of the respiratory gas; an end-tidal partial pressure of the respiratory gas; a positive end-expiratory pressure for ventilating the patient.

The minute volume can be understood as the product of the tidal volume and the respiratory rate.

Alveolar ventilation (V̇ _A ) can be understood as the product of the respiratory rate and the difference between the tidal volume and the (anatomical) dead space.

The term "airway dead space" can generally be understood as a portion of the tidal volume that remains in the airways with each breath and thus does not reach the alveolar compartment.

The end-tidal partial pressure of the respiratory gas can be understood as the concentration of the respiratory gas in the patient's exhaled airflow at the end of a single breath.

The mixed expiratory partial pressure of the respiratory gas can be understood as a specific fraction of the end-tidal partial pressure and/or, for example, as an average concentration of the respiratory gas in the airflow exhaled by the patient during a single breath.

According to one embodiment, the volume-dependent curve can be divided into at least three successive characteristic exhalation phases during a single breath of the patient. In this case, the various parameters can include at least one of the following: a first respiratory gas volume as a volume of respiratory gas exhaled by the patient in a first (e.g., earliest) of the exhalation phases; a second respiratory gas volume as a volume of respiratory gas exhaled by the patient in a second (e.g., middle) of the exhalation phases; a third respiratory gas volume as a volume of respiratory gas exhaled by the patient in a third (e.g., last or penultimate) of the exhalation phases; an (e.g., average) slope of the volume-dependent curve in at least one of the exhalation phases, in particular in a last (or penultimate) of the exhalation phases.

The middle exhalation phase can be understood as an exhalation phase that lies between the earliest and the last (or penultimate) exhalation phase. It is possible that the first exhalation phase transitions directly into the second exhalation phase and/or the second exhalation phase transitions directly into the third exhalation phase.

The term "slope" can be understood as a (for example, average) rate of change in the concentration of the breathing gas in the breathing gas stream, related to the respective exhalation phase(s). The slope can also be appropriately normalized to obtain a normalized slope, which can then be used as one of the parameters.

The exhalation phases may have been determined, for example, using the aforementioned mathematical function and/or according to Fowler's method. These exhalation phases may be the typical (three or four) phases of a volumetric capnogram or oxigram. The exhalation phases may differ significantly in their length and/or in the (e.g., average) slope of the volume-dependent curve.

For example, in the case of a capnogram, the first exhalation phase can extend from the beginning of exhalation to a first point where the rate of change of the second derivative of the volume-dependent curve reaches its maximum, or where the third derivative of the volume-dependent curve reaches its left-hand maximum. The second exhalation phase can extend from the first point to a second point where the third derivative of the volume-dependent curve reaches its right-hand maximum. The third exhalation phase can extend from the second point to the end of exhalation. The term "volume-dependent curve" can also refer to an approximation, for example, in the form of the aforementioned mathematical function.

The first exhalation phase can be the earliest phase of exhalation (10% to 12% of the total breath), in which there is little or no carbon dioxide in the exhaled air. The second exhalation phase can be the phase of the greatest (average) increase in the carbon dioxide concentration in the exhaled airstream (15% to 18% of the total breath). The third exhalation phase can be a phase in which the carbon dioxide concentration in the exhaled airstream—in contrast to the preceding exhalation phases—is predominantly determined by gases coming from the alveoli (70% to 75% of the total breath).

According to one embodiment, at least one normalized respiratory gas volume can be determined by dividing one of the three respiratory gas volumes by the total volume (of respiratory gas or of air containing respiratory gas) exhaled by the patient during a single breath. In this case, the parameters can include the at least one normalized respiratory gas volume in addition to or as an alternative to the respective (non-normalized) first, second, or third respiratory gas volume.

According to one embodiment, the machine learning module can comprise an artificial neural network. Accordingly, the weights of the machine learning module can be weights of the artificial neural network. The artificial neural network can include an input layer for inputting the input data, an output layer for outputting the output data, and at least one trainable intermediate layer between the input and output layers. For example, the artificial neural network can include at least one of the following network types: a multilayer perceptron, a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), or a long short-term memory (LSTM). For example, the artificial neural network can include at most 30, at most 15, or at most 5 trainable intermediate layers. Such a network architecture is particularly computationally efficient and yet enables sufficiently accurate estimation.

According to one embodiment, the artificial neural network can be implemented as an adaptive neuro-fuzzy inference system (ANFIS). Such an inference system can be based on a Takagi-Sugeno controller and/or a Tsukamoto controller and/or include a set of fuzzy if-then rules that can be trained to approximate nonlinear functions. The architecture of the ANFIS can, for example, include five layers, including a so-called fuzzification layer as one of the first of the five layers. This embodiment makes it possible to combine the advantages of an artificial neural network with the advantages of fuzzy logic in a single framework.

According to one embodiment, the measurement data can include at least first and second measurement data. In this case, the first measurement data can show a volume-dependent profile of the concentration of a first respiratory gas in the inhaled airflow as a function of the inhaled air volume, and the second measurement data can show a volume-dependent profile of the concentration of a second respiratory gas in the inhaled airflow, which differs from the first, as a function of the inhaled air volume. Accordingly, the output data can show the concentration of the first and second respiratory gases in the patient's blood. This embodiment allows for the simultaneous estimation of the concentrations of different respiratory gases in the patient's blood, for example, carbon dioxide and oxygen.

According to one embodiment, the input data can include first input data generated from the first measurement data and second input data generated from the second measurement data. The first input data can be fed into a first artificial neural network, and the first output data, indicating the concentration of the first exhaled gas in the patient's blood, can be output by the first artificial neural network. Similarly, the second input data can be fed into a second artificial neural network, and the second output data, indicating the concentration of the second exhaled gas in the patient's blood, can be output by the second artificial neural network. Accordingly, the output data can include both the first and second output data. The weights of the machine learning module can, for example, be weights of the first and second artificial neural networks, respectively. The two artificial neural networks can differ from each other in their architecture and/or weights, or they can be identical and/or have been trained separately. For example, at least one or each of the two artificial neural networks can be architecturally identical to the network described above and/or implemented as an adaptive neuro-fuzzy inference system. Alternatively, the first and second input data can be fed into a common artificial neural network, such as the one described above.

For example, the initial input data could be a matrix of initial values for different The first parameters may include those relating to the volume-dependent behavior of the first measurement data, and/or the second input data may comprise a matrix of two values for different second parameters relating to the volume-dependent behavior of the second measurement data. The first parameters may be the same number and/or type as the second parameters, and/or they may differ from the second parameters. It is possible that at least one of the first values is determined using a first mathematical function that approximately defines at least one section of the volume-dependent behavior of the first measurement data (e.g., from different time steps), and/or that at least one of the second values is determined using a second mathematical function that approximately defines at least one section of the volume-dependent behavior of the second measurement data (e.g., from different time steps).

Brief description of the drawings

• 1 zeigt ein Beatmungsgerät gemäß einer Ausführungsform der Erfindung.
• 2 zeigt ein Flussdiagramm zur Veranschaulichung einer Ausführungsform eines Verfahrens zum Schätzen einer Konzentration eines Atemgases im Blut eines Patienten.
• 3 zeigt ein Flussdiagramm zur Veranschaulichung einer Ausführungsform eines Verfahrens zum Trainieren eines Machine-Learning-Moduls für ein Beatmungsgerät.
• 4 zeigt ein Kapnogramm zur Verwendung in einem Verfahren gemäß einer Ausführungsform der Erfindung.
• 5 zeigt ein Machine-Learning-Modul zur Verwendung in einem Verfahren gemäß einer Ausführungsform der Erfindung.
• 6 zeigt ein Flussdiagramm zur Veranschaulichung der Erzeugung von Trainingsdatensätzen in einem Verfahren gemäß einer Ausführungsform der Erfindung.

The following describes embodiments of the invention with reference to the accompanying drawings. Neither the description nor the drawings are to be understood as limiting the scope of the invention.

• 1 shows a ventilator according to an embodiment of the invention.
• 2 Figure 1 shows a flowchart illustrating one embodiment of a method for estimating the concentration of a respiratory gas in a patient's blood.
• 3 shows a flowchart illustrating one embodiment of a method for training a machine learning module for a ventilator.
• 4 shows a capnogram for use in a method according to an embodiment of the invention.
• 5 shows a machine learning module for use in a method according to an embodiment of the invention.
• 6 Figure 1 shows a flowchart to illustrate the generation of training data sets in a method according to an embodiment of the invention.

The figures are purely schematic and not to scale. If the same reference symbols are used in different drawings, these reference symbols denote identical or equivalent features.

Embodiments of the invention

1 Figure 1 shows a ventilator 1 for invasive and/or non-invasive ventilation of a patient. The ventilator 1 comprises a sensor 3 for generating measurement data 5 and a data processing device 7 with a memory 9 and a processor 11 configured to execute a computer program stored in the memory 9 for processing the measurement data 5 in at least one of the methods described below.

The measurement data 5 define a volume-dependent trend 13 (see 4 ) a concentration of a respiratory gas (e.g. _pCO2 ) in a patient's exhaled airflow depending on the patient's exhaled air volume.

In particular, the measurement data 5 can display the volume-dependent curve 13 related to a single breath of the patient. Furthermore, it is possible for the measurement data 5 to display a positive end-expiratory pressure (PEEP) for ventilating the patient, corresponding to the volume-dependent curve 13.

For example, the measurement data 5 - as in 4 shown – a capnogram generated non-invasively by volumetric capnography, indicating the volume-dependent course 13 of the concentration of carbon dioxide as the breathing gas. Additionally or alternatively, the measurement data 5 can encode an oxigram generated non-invasively by volumetric oxigraphy, indicating the volume-dependent course 13 of the concentration of oxygen as the breathing gas.

2 Figure 1 shows an example of a procedure for estimating the concentration of the respiratory gas in the patient's blood. The procedure is executed by the data processing device 7 and comprises the following steps.

In step 200, the measurement data 5 are received.

In step 202, input data 14 is generated from the measurement data 5 (see also 5 ). The input data 14 comprise a matrix of values for various parameters regarding the volume-dependent trend 13.

In step 204, the input data 14 are fed into a machine learning module 15, which has been trained to convert the input data 14 into output data 17 that indicate the (estimated) concentration of the respiratory gas in the patient's blood (under "module" the following can be pre- and below, a software and/or hardware module). For example, the output data 17 may include an estimate for the arterial partial pressure of carbon dioxide ( _{pa CO2} ) and/or an estimate for the arterial partial pressure of oxygen ( _{pa O2} ).

In step 206, the output data 17 is output by the machine learning module 15.

The output data 17 can then be further processed, for example to display the output data 17 in a suitable manner on a display of the ventilator 1 and/or to send it via a wireless and/or wired data communication connection to an external device such as a server, a PC, a laptop, a tablet, a smartphone or a smartwatch.

3 Figure 1 shows an example of a procedure for training the machine learning module 15. It is possible for the procedure to also be executed by the data processing device 7. Additionally or alternatively, the procedure can be executed by another data processing device outside of the ventilator 1. The procedure comprises the following steps.

In step 300, several measurement data sets 19, each comprising the measurement data 5, are received (see also 6 The measurement data 5 of the various measurement data sets 19 are at least partially assigned to different patients 21. For example, the measurement data sets 19 may have been generated during mechanical ventilation of the patients 21 in several controlled ventilation steps 23 of a specific duration, e.g., 2 min, 5 min, or 10 min. Each measurement data set 19 may contain the measurement data 5 for exactly one breath of the respective patient 21.

In step 302, several training datasets 25 are generated from the measurement datasets 19 of the various patients 21, with each training dataset 25 being assigned to one of the patients 21 and comprising a matrix of values for various parameters relating to the respective volume-dependent course 13. The training datasets 25 can be generated, for example, by a suitably configured selection module 27, which can be implemented as another machine learning module. It is possible that only measurement datasets 19, each generated in a specific time period of the ventilation steps 23, for example, in the last minute, as in 6 shown. With a controlled breath duration of 2 s, this results in 30 measurement data sets per ventilation step 23 for each patient 21. From each of these measurement data sets 19, for example, a training data set 25 can then be generated.

In step 304, each training data set 25 is entered as input data 14 into the machine learning module 15. The machine learning module 15 converts the input data 14 into output data 17, which displays the (estimated) concentration of the respiratory gas in the blood of the respective patient 21.

In step 306, the output data 17 is output by the machine learning module 15.

In step 308, a deviation of the output data 17 from target data 29, which are assigned to the respective training data set 25 or the respective input data 14, is determined.

Finally, in step 310, the weights of the machine learning module 15 are adjusted in an optimization procedure to reduce the deviation, for example in a gradient method with backpropagation.

Steps 308 and 310 can, for example, be performed by a suitably configured optimization module 31.

It is advisable to train machine learning module 15 in several consecutive time steps until the deviation reaches an acceptable value. For example, steps 304 to 310 can be executed in each time step.

As in 5 and 6 As shown, the matrix can be a one-dimensional vector and/or include values for at least 2, at least 5, at least 10 different parameters, and especially for 11 or 12 different parameters. The number of parameters should not be too large, for example, not greater than 15, 20, or 30, to keep the consumption of computing resources low. The number of values in the matrix can match the number of different parameters; that is, each value in the matrix can define exactly one of the different parameters.

Depending on the specific design, the various parameters may include at least one of the following parameters relating to the individual patient (see also 4 ): a total volume of respiratory gas exhaled by the patient in a single breath; a total volume of respiratory air containing the respiratory gas exhaled by the patient in a single breath, also Tidal volume (V _{_T} ); minute volume; alveolar ventilation; airway dead space (V_{_Daw}); mixed expiratory partial pressure of the respiratory gas (e.g., p_{_E̅}CO_₂); end-tidal partial pressure of the respiratory gas (e.g., p_{_et}CO_₂); positive end-expiratory pressure for ventilating the patient.

Furthermore, the volume-dependent curve 13 of the respective measurement data 5 can be subdivided into at least three consecutive characteristic exhalation phases during a single breath of the respective patient. In this case, the various parameters can include at least one of the following: a first respiratory gas volume as a volume of respiratory gas exhaled by the patient in a first exhalation phase I; a second respiratory gas volume as a volume of respiratory gas exhaled by the patient in a second exhalation phase II; a third respiratory gas volume as a volume of respiratory gas exhaled by the patient in a third exhalation phase III; an average slope of the volume-dependent curve 13 in at least one of the exhalation phases I, II, III, particularly in the third exhalation phase III.

Optionally, the three respiratory gas volumes can each be normalized by dividing by the tidal volume. In this case, the various parameters can encompass the respective normalized respiratory gas volume.

The values for these parameters can be calculated, at least partially, from the measurement data 5 using a special mathematical function 33. The mathematical function 33 can also be used to determine the three exhalation phases I, II, and III. Alternatively, the various parameters can be determined, at least partially, using an additional machine learning module.

The mathematical function 33 can be an approximation of at least one segment of the volume-dependent curve 13. This approximation can be determined, for example, by a regression analysis of measurement data 5 acquired in several successive time steps, e.g., over multiple breaths, with respect to a patient. A particularly accurate and computationally efficient approximation can be achieved if the mathematical function 33 is determined according to the Levenberg-Marquardt algorithm.

The machine learning module 15 can, for example, be considered an artificial neural network 35 (see 5 The machine learning module 15 can be implemented with an input layer 37 for inputting the input data 14, an output layer 39 for outputting the output data 17, and at least one trainable intermediate layer 41 for converting the input data 14 into the output data 17. Alternatively, the machine learning module 15 can be implemented as a combination of several artificial neural networks. Other machine learning algorithms are also possible, such as a decision tree, a random forest, a k-nearest neighbors algorithm, a support vector machine, a Bayesian classifier, a k-means algorithm, a genetic algorithm, a kernel regression algorithm, or a discriminant analysis algorithm.

To enable rapid training—for example, on the ventilator 1—and the use of cost-effective hardware, the network 35 should not be overly complex. An architecture with a maximum of 10 trainable intermediate layers 41 proved to be a good compromise between efficiency and accuracy in experiments. A network 35 in the form of an adaptive neuro-fuzzy inference system (ANFIS) is particularly advantageous. However, significantly more complex DNN architectures are also possible.

The machine learning module 15 can also be trained to simultaneously estimate the concentration of different respiratory gases in the patient's blood.

In this case, the measurement data 5 can, for example, comprise first measurement data and second measurement data, where the first measurement data show a volume-dependent profile 13 of a concentration of a first respiratory gas, for example, carbon dioxide, in the respiratory airflow as a function of the respiratory air volume, and the second measurement data show a volume-dependent profile of a concentration of a second respiratory gas, for example, oxygen, in the respiratory airflow as a function of the respiratory air volume. Accordingly, the training data sets 25 or the input data 14 can be generated from the first and second measurement data, and the output data 17 can show an (estimated) concentration of the first and second respiratory gases in the blood of the respective patient.

For this purpose, the input data 14 can be fed either into a common artificial neural network 35 or into two separately trainable or trained artificial neural networks. In the second case, for example, the first measurement data can be used to generate initial input data for a first network, and the second measurement data can be used to generate input data for a second network. Accordingly, initial output data, which indicates the concentration of the first breath gas in the patient's blood, is generated by the first network and the second Output data showing the concentration of the second respiratory gas in the patient's blood, output through the second network.

The training of Machine Learning Module 15 can, for example, include the following steps.

First, blood samples are taken from various patients, and the time of collection is noted for each blood sample.

The blood samples are analyzed to determine the actual arterial partial pressure of carbon dioxide and/or oxygen of the respective patient.

Simultaneously with and/or shortly before and/or shortly after blood sampling, the partial pressure of carbon dioxide or oxygen in the patient's exhaled airflow is measured by sensors over several breaths, at least during exhalation. Additionally, the airflow rate is measured by sensors, at least during exhalation.

By integrating the flow, the volume of air exhaled by the patient is determined for each breath.

From the time-based profiles of the carbon dioxide and oxygen partial pressures in the breathing airflow and the breathing air volume, corresponding volumetric capnograms and oxigrams are generated.

Subsequently, in an optimization procedure, for example using regression analysis, a mathematical function is determined that approximately defines the curves in the capnograms or oxigrams.

Based on the mathematical function, various parameters are then determined that describe certain characteristics of the curves.

These parameters, along with the respective values of the actual arterial carbon dioxide and oxygen partial pressures, are used to train the machine learning module 15.

The practicality of the procedure was confirmed, among other things, in the following experiment.

In 14 lung-washed animals, the arterial partial pressure of carbon dioxide ( _{pa CO₂} ) was continuously recorded using an optical intravascular catheter. Simultaneously, a capnogram was recorded with each breath. The animals were mechanically ventilated with fixed settings, with the positive end-expiratory pressure varied in several steps from 0 to 22 _cmH₂O . The resulting 8599 data points—each paired with a pA _{CO₂ value} and a set of 12 parameters derived from the capnogram of a breath—were inputted to an ANFIS model. The data points were split into an initial set of 7370 data points (85%) for training the model and a second set of 1229 data points (15%) for testing the trained model. The ANFIS analysis was repeated in 10 independent steps, with the input data points randomly selected in each step.

The Bland-Altman plot for the 10 independently tested ANFIS models showed a mean deviation of 0.03 ± 0.03 mmHg between the estimated and actual p_{_a}CO_₂ values, with a range of agreement of 2.25 ± 0.42 mmHg and a mean squared deviation of 1.15 ± 0.06 mmHg. The estimate was therefore sufficiently accurate. A concordance index of 95.5% (four-quadrant plot) and 94.3% (polar plot) was calculated, indicating good trend tracking ability.

The advantages of the procedure can be attributed to, among other things, the following points.

Initially, volume-based measurement data are used instead of time-based measurement data. In particular, capnography and oxigraphy allow for the monitoring of numerous informative parameters regarding gas exchange. These parameters provide a more robust data basis for assessing gas exchange than purely time-based measurements of exhaled respiratory gas flow.

Furthermore, it was shown that the parallel evaluation of several parameters leads to a more accurate estimate than the evaluation of a single parameter (e.g., the end-tidal carbon dioxide partial pressure).

The use of a machine learning module also offers the advantage that non-linear, hidden information related to pulmonary gas exchange can be taken into account with each breath.

Finally, it should be noted that terms such as "have", "comprise", "include", "with", etc. do not exclude any other elements or steps, and indefinite articles such as "a" or "an" do not exclude any variety.

Furthermore, it is pointed out that features or steps that refer to one of the previous The embodiments described above can also be used in combination with features or steps described with reference to other embodiments described above.

Reference numerals in the claims are not to be understood as limiting the scope of the subject matter defined by the claims.

List of reference symbols

1

ventilator

3

Sensors

5

Measurement data

7

Data processing device

9

memory

11

processor

13

volume-dependent course

14

Input data

15

Machine learning module

17

Output data

19

Measurement data set

21

patient

23

Ventilation step

25

Training dataset

27

Selection module

29

Target data

31

Optimization module

33

mathematical function

35

artificial neural network

37

Input layer

39

Output layer

41

Intermediate shift

200

Receiving measurement data

202

Generating input data

204

Entering input data

206

Output data

300

Receiving measurement data sets

302

Generating training datasets

304

Entering input data

306

Output data

308

Determining a deviation

310

Adjusting weights

Aw-alv

Turning point (border between airways and alveoli)

I

first exhalation phase

II

second exhalation phase

III

third exhalation phase

pCO2

Carbon dioxide partial pressure

paCO2

arterial carbon dioxide partial pressure

pACO2

alveolar carbon dioxide partial pressure

petCO2

end-tidal carbon dioxide partial pressure

pE̅CO2

mixed expiratory carbon dioxide partial pressure

V

Breathing air volume

VDaw

Airway dead space

VT

Tidal volume

VTalv

alveolar tidal volume

Claims (15)

A computer-implemented method for estimating the concentration (p _a CO ₂ ) of a respiratory gas in the blood of a patient (21), comprising: receiving (200) measurement data (5) showing a volume-dependent profile (13) of the concentration (pCO ₂ ) of the respiratory gas in a breath stream exhaled by the patient (21) as a function of the volume (V) of breath exhaled by the patient (21); generating (202) input data (14) from the measurement data (5), wherein the input data (14) comprise a matrix of values for various parameters relating to the volume-dependent profile (13); inputting (204) the input data (14) into a machine-learning module (15) trained to convert the input data (14) into output data (17), wherein the output data (17) indicate a concentration (p _a CO ₂ ) of the respiratory gas in the blood of the patient (21); Output (206) of the output data (17) by the machine learning module (15). Computer-implemented method for training a machine learning module (15) for a medical device (1), wherein the method comprises: receiving (300) several measurement data sets (19), each comprising measurement data (5) that show a volume-dependent course (13) of a concentration ( _pCO2 ) of a respiratory gas in an exhaled airflow from a patient (21) as a function of an exhaled air volume (V) from the patient (21), wherein the measurement data (5) of different measurement data sets (19) are at least The following steps are performed: 302. The training data sets (25) are partially assigned to different patients (21); 302. The machine learning module (15) generates several training data sets (25) from the measurement data sets (19), wherein each training data set (25) is assigned to one of the patients (21) and comprises a matrix of values for various parameters relating to the volume-dependent course (13); 304. The machine learning module (15) inputs each training data set (25) as input data (14) into the machine learning module (15), which is configured to convert the input data (14) into output data (17), wherein the output data (17) indicates a concentration (p _a CO ₂ ) of the respiratory gas in the blood of the respective patient (21); 306. The machine learning module (15) outputs the output data (17); 308. The machine learning module determines a deviation of the output data (17) from target data (29) assigned to the respective training data set (25); Adjusting (310) weights of the machine learning module (15) in an optimization procedure to reduce the deviation. Method according to any of the preceding claims, whereby the measurement data (5) indicate the volume-dependent curve (13) with respect to a single breath of the patient (21); and/or whereby the measurement data (5) further indicate a positive end-expiratory pressure associated with the volume-dependent curve (13) for ventilating the patient (21). Method according to one of the preceding claims, wherein a mathematical function (33) which approximately defines at least one section of the volume-dependent curve (13) is determined using the measurement data (5), wherein at least one of the values in the matrix is calculated using the mathematical function (33). Procedure according to Claim 4 , wherein the measurement data (5) were generated in several successive time steps and the mathematical function (33) is determined using the measurement data (5) from different time steps; and/or wherein the mathematical function (33) is determined according to the Levenberg-Marquardt algorithm. A method according to any of the preceding claims, wherein the various parameters comprise at least one of the following parameters: a total volume of the respiratory gas exhaled by the patient (21) in a single breath; a total volume (V_{_T} ) of respiratory air containing the respiratory gas exhaled by the patient (21) in a single breath; a minute ventilation; an alveolar ventilation; an airway dead space (V _{_Daw} ); a mixed expiratory partial pressure (p _{_E̅} CO _₂ ) of the respiratory gas; an end-tidal partial pressure (p_{_et}CO_₂ ) of the respiratory gas; a positive end-expiratory pressure for ventilating the patient (21); and/or wherein the volume-dependent curve (13) is subdivided into at least three successive characteristic exhalation phases (I, II, III) during a single breath of the patient (21), wherein the various parameters include at least one of the following: a first respiratory gas volume as a volume of respiratory gas exhaled by the patient (21) in a first (I) of the exhalation phases (I, II, III); a second respiratory gas volume as a volume of respiratory gas exhaled by the patient (21) in a second (II) of the exhalation phases (I, II, III); a third respiratory gas volume as a volume of respiratory gas exhaled by the patient (21) in a third (III) of the exhalation phases (I, II, III); a slope of the volume-dependent curve (13) in at least one of the exhalation phases (I, II, III), in particular in a last (III) of the exhalation phases (I, II, III). Procedure according to Claim 6 , wherein at least one normalized respiratory gas volume is determined by dividing one of the three respiratory gas volumes by a total volume (V _T ) of respiratory air containing the respiratory gas exhaled by the patient (21) during a single breath, wherein the parameters include the at least one normalized respiratory gas volume. Method according to one of the preceding claims, wherein the machine learning module (15) comprises an artificial neural network (35). Procedure according to Claim 8 , wherein the artificial neural network (35) is implemented as an adaptive neuro-fuzzy inference system. Method according to one of the preceding claims, wherein the measurement data (5) comprise at least first measurement data and second measurement data, wherein the first measurement data indicate a volume-dependent profile (13) of a concentration ( _pCO2 ) of a first respiratory gas in the respiratory airflow depending on the respiratory air volume (V) and the second measurement data indicate a volume-dependent profile (13) of a concentration of a second respiratory gas in the respiratory airflow depending on the respiratory air volume ( _V ); wherein the output data (17) indicate a concentration ( _paCO2 ) of the first respiratory gas and a concentration of the second respiratory gas in the patient's blood (21). Procedure according to Claim 10 , wherein the input data (14) comprise first input data generated from the first measurement data and second input data generated from the second measurement data; wherein the first input data are fed into a first artificial neural network and first output data indicating the concentration (p _a CO ₂ ) of the first breath gas in the patient's blood (21) are output by the first artificial neural network; wherein the second input data are fed into a second artificial neural network and second output data indicating the concentration of the second breath gas in the patient's blood (21) are output by the second artificial neural network; wherein the output data (17) comprise the first output data and the second output data. Data processing device (7) comprising a processor (11) configured to perform at least one of the methods according to any of the preceding claims. Medical device (1), comprising: a sensor system (3) for generating measurement data (5) indicating a volume-dependent profile (13) of a concentration ( _pCO2 ) of a respiratory gas in an exhaled airflow from a patient (21) depending on a volume of air (V) exhaled by the patient (21); a data processing device (7) according to Claim 12 . Computer program, comprising instructions that cause a processor (11), when executing the computer program, to use at least one of the methods according to one of the Claims 1 until 11 to execute. Computer-readable medium on which the computer program is stored. Claim 14 is stored.